A Novel Load Balanced and Lifetime Maximization ... - CiteSeerX

A novel Load Balanced and Lifetime Maximization Routing Protocol in Wireless Sensor Networks Chengjie Wu∗

Ruixi Yuan†

Hongchao Zhou‡

Center for Intelligent and Networked Systems, Dept. of Automation, TNLIST Lab Tsinghua University, Beijing 100084, China ∗ [email protected] † [email protected] ‡ [email protected]

Abstract— Balancing energy consumption and prolonging network lifetime are open challenges in Wireless Sensor Networks. In this paper, we design a novel load balanced routing protocol to maximize lifetime of Wireless Sensor Networks (WSNs). In order to balance the energy consumption among sensor nodes, we deploy multiple sinks simultaneously which are connected though wired or wireless infrastructure. We introduce a potential model and propose a routing scheme in which sensor nodes construct routes based on local topology information and the state information from sinks’ broadcast messages. Sinks monitor their traffic load and adjust their own parameters to balance the traffic load in the network. Theoretical analysis and simulation results show that our protocol can significantly improve the performance of the system in following aspects: robustness, lifetime and reliability.

I. I NTRODUCTION Wireless sensor networking is a promising technology which has a wide variety of potential applications such as ocean and wildlife monitoring, manufacturing machinery performance monitoring, building safety and earthquake monitoring, and many military applications. Wireless Sensor Networks with Single Sink (WSNSS) are deployed in many applications. However, WSNSS confronts several limitations including robustness, scalability, reliability owning to their severe dependence on the single sink. Once the only sink stops working or carrys too high traffic load, the system will be corrupted. All these limitations make WSNSS venerable in real applications. Wireless Sensor Networks with Multiple Sinks(WSNMS) are often used to solve these problems via their significant advantages. A large number of papers have investigated in WSNMS. These papers can be classified into two categories: WSNs with mobile sinks [1], [2], [3] or stationary sinks [4] [5] [6]. The mobility of sinks leads to frequent re-establishments of routes from sources to sinks, which cause extra energy consumption and high end-to-end delay. Thus, WSNs with multiple stationary sinks (WSNMSS) are adopted in many applications. In routing protocols designed for WSNMSS, a routing protocol described as NearestSink is usually adopted [4] [5] [6]. In this protocol, sensor nodes choose the nearest sink as their destination of data packets. Admittedly, since it ensures packets reach sinks through the smallest number of hops, this scheme pursues the least energy consumption in the whole network. However, in some cases, the geographically asymmetric generation of event data packets leads to asymmetric energy consumption. Nodes near the sink with higher

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traffic will deplete their energy earlier and part of the network will lose function. In a recent paper [7], Suzuki et. al try to solve this problem by proposing a scheme called DispersiveCast. In this scheme, once the difference between the traffic of two sinks exceeds the predetermined threshold, the sinks will command the sensor nodes in hot area to send their packets to two sinks with probabilities. However, opposite to the distributed nature of wireless sensor networks, the centralized characteristic of this scheme limits its scalability in implementation. Furthermore, based on the fact that sending ratio is calculated just by the number of hops, this scheme could not be sufficiently effective in operation. In [8], authors introduce partial differential equations which are analogous to the Maxwells equations in electrostatic theory into routing problems. They formulate route optimization problems in WSNs by a vector field model and define the total communication cost in the network as the integral of a quadratic form of the vector field over the network area. They partition the network into areas, each corresponding to one of the destinations based on the concept ”potential function”. Different from our protocol, this paper aims to minimize the energy cost in the network rather than prolong the lifetime. Moreover, it is also a centralized algorithm which is limited in scalability and complexity. In this paper, we propose a new routing protocol named Load Balanced and Lifetime Maximization Routing (BLM) which can greatly improve the performance of network in Robustness, Reliability and Lifetime. In BLM, rather than choose the nearest sink to communicate, sensor nodes will choose relay nodes based on its neighbors’ Potential Function [8]. Unlike traditional routing methods, which are usually limited by local topological knowledge, our protocol is adaptive to the change of network topology for the additional Global Informative Mechanism (GIM). By BLM, we can balance energy consumption and data traffic over the entire network, and ensure robust, reliable as well as survival of the network. The remainder of this paper is organized as follows. We analyze the system model and formulate the problem in Section II. We describe our protocol in Section III. The theoretical analysis of our protocol is presented in Section IV. The simulation results are shown in Section V. Section VI concludes the paper.

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II. S YSTEM M ODEL AND P ROBLEM F ORMULATION Our system is designed for WSNs consisting of hundreds or thousands of sensor nodes in large scale applications such as monitoring in remote area, fire detection in forest and so on. In such applications, sensor nodes are densely deployed in large area. Once deployed, nodes can never be recharged or replaced. They execute tasks including idle listening, event sensing and relaying packets. After depleting their energy, nodes turn to death and stop working. Since network cannot accomplish assigned mission after nodes died, the lifetime of WSNs becomes a crucial parameter to evaluate performance of routing protocols. The maximization of lifetime can be formulated as an optimization problem. The variables of this optimization problem are routing parameters at nodes. When having sensed or asked to relay a data packet, each node needs to transmit this packet to a sink. However, it can not send the packet directly to sinks except that it is a sink’s neighbor. So normally a node needs to choose a neighbor sensor as its next hop. Nodes’ chosen of the next hop will influence the energy consumption of the network as well as the lifetime. In the optimization problem, a wireless sensor network is modeled as a graph G(V, A) where: 1) V = VN ∪ VS , VN and VS represent the set of sensor nodes and sinks respectively. 2) A ⊆ V × V represents the set of wireless links in the network. We denote the current energy of sensor node i as Ei . Meanwhile, We denote the neighborhood of sensor node i as Ni = {j : (i, j) ∈ A, j ∈ V}

(1)

To guarantee every event can be sensed by the sensor network, all sensor nodes in the network should be active. So we define the Lifetime of the network L as the shortest lifetime of sensor nodes in the network: L = min{Ti : Ti = T (Ei = 0) − Tstart ,

i ∈ VN }

(2)

where L denotes the lifetime of the network, Tstart denotes the time when system starts to work, T (Ei = 0) denotes the time when sensor node i dies. In the definition of Lifetime, the minimum of Ti is considered. However, which node has the minimum T depends on the operation of the system. For simplification, we transform the lifetime maximization problem to residual energy maximization problem in given time. Then, the objective of our work is to: M aximize η subject to the constraints:


xij −

j∈Ni




xji

=

Si (T ),

xij

≥

η,

∀i ∈ V

(3)

j∈Ni

Ei − Et




j∈Ni




i∈VS

Si (T ) = − xij

≥

0,

∀i ∈ VN


(4)

Si (T )

(5)

∀i, j ∈ V

(6)

i∈VN

where xij and xji represent the number of data packets forwarded from node i to node j and from j to i respectively. Si (T ) represents the number of data packets originally sensed by sensor node i and is a known parameter in the problem. Et is the energy consumption to transmit a data packet. η is the minimum residual energy of sensor nodes after operation. {xij , i, j ∈ VN } are variables in this optimization problem. Under the constraints that all data packets sensed by sensor nodes should be transmitted to sinks, our objective is to maximize the minimum residual energy of sensor nodes. To solve such optimization problem, {Si (T ), i ∈ V} should be known in advance. However, because of the decentralized characteristic of WSN, {Si (T ), i ∈ V} are not available at sinks. Moreover, since we cannot forecast the occurrence of events, {Si (T ), i ∈ V} are unpredictable. Therefore, we cannot give a solution in real applications. Instead, We propose a distributed routing protocol which is near optimal. III. BLM P ROTOCOL A. Overview In our work, we implement a distributed routing algorithm and a global informative mechanism. Before the description of protocol, we present our potential model at first. In our potential model, each sink is assumed as a stationary positive charge with variable electrical quantity and infinite mass. Data packets are assumed as negative charges with given electrical quantities and mass. Once a sensor node has sensed an event, it will generate a data packet, which will be treated as a negative charge. Then, there will be two basic forces acted on it, Electrical attraction and Gravity. Since sinks are assumed as positive charges, a nature electrical attraction force will generate between a positive charge and a negative charge, whose value is determined by electrical quantity of charges and distance between them. Gravity derives form energy difference between sensor nodes. Each sensor node’s current energy is transformed to a height. The less energy a node has, the higher its height is. Negative charges will naturally move from higher nodes to lower nodes. Negative charges’ movement are determined by composition of these two forces. Accordingly, we can analyze these two forces by Electrical Potential, Gravitational Potential and their summation - Potential Function. Finally packets will move following the direction that Potential decreases most and reach sinks whose potential value are the lowest. However, local routing algorithm risks functional failure for its localized vision. For example, when a sink failed, nodes may still choose this sink as their destination. To avoid such local blind vision problem, a global informative mechanism is implemented. In the mechanism, sinks broadcast global information to nodes. According to such information, nodes adjust their routes. To implement this mechanism effectively, dual communication channels are necessary. Both the sensor nodes and the sink nodes are stationary and equipped with transceivers that can support dual channels with different frequencies. Nodes send data packets to sinks through data

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channel. Sinks broadcast control packets through control channel. Then we will present our protocol, which is divided into two phases: Topology Discovery & Routing Maintenance and Global Informative Mechanism

0 −1000 −2000 −3000 −4000

B. Topology Discovery & Routing Maintenance

−5000

This phase contains three steps: Global Notification, Local Identification and Routing Maintenance. Global Notification step is the start of BLM protocol. At the beginning, each sink broadcasts its electrical quantity, which is determined by energy capacity of its neighbor nodes, through control channel. Each node obtains the distances from itself to sinks by analyzing the electrical quantity messages. Node i will record the distance from itself to sink j as {Dij , j ∈ VS } and record sinks’ electrical quantities as {Qj , j ∈ VS }. Then, each node will calculate its Electrical Potential related to sinks respectively following classical model. For example, Node i records its Electrical Potential related to sink j as T (i, j): Qj , i ∈ VN , j ∈ VS T (i, j) = − Dij

(7)

The Electrical Potential, Gravitational Potential and Potential Function of node i are denoted as T (i), G(i) and P (i), which are calculated by following equations:
T (i, j) (8) T (i) = j∈VS

G(i) P (i)

= −Ei = α × T (i) + β × G(i),

(9) ∀i ∈ VN

(10)

where α and β are two constant parameters. Since the process of calculating potential does not cost much time, after a certain period of time, all sensor nodes will finish their calculation. We present the potential value of sensor nodes in Fig.1. Then, the system will turn into Local Identification step. Each node will communicate with its neighbors and record their potential values. The routes of each node depends on its and its neighbors’ potential value. To achieve a better performance, we propose an energy effective algorithm for each node to keep its neighbors’ real time potential value. Since transmission of packets consumes the biggest part of nodes’ energy. Each node can maintain its neighbors’ potential value until its neighbors transmit packets. When transmitting packets, each sensor node will add its potential value in packet head. The packet head of a data packet is shown here. Source ID Potential Destination ID After that, the node’s neighbors will hear this packet and update their potential tables. Thus each node can keep its neighbors’ real-time potential value without great energy consumption. After Global Notification and Local Identification, the network will turn into Routing Maintenance step. Data packets sensed by sensor nodes will be routed to sinks based on potential value table. Once a node has sensed an event or plans to relay a data packet, it will choose the neighbor with

−6000 −7000 30 25 20 15 10 5

Fig. 1.

5

10

15

20

25

30

Potential value of sensor nodes

the lowest potential value as the next hop. As explained in the potential model, data packets will automatically flow to nodes with low potential value, and finally reach sinks whose potential value are the lowest. C. Global Informative Mechanism To avoid local blind vision problem mentioned above(IIIA), we implement a global informative mechanism in our work. In this mechanism, sinks monitor system’s performance and inform nodes with global information to adjust nodes’ behavior. The crucial concept of this mechanism are the electrical quantities of sinks. The system tries to adjust nodes’ behavior by tailoring sinks’ electrical quantities. There are three schemes contained in Global Informative Mechanism(GIM):Negative Feedback, Sink Failure Announcement and Congestion Control. To balance the energy consumption and prolong lifetime of the network, we introduce Negative Feedback scheme. In this scheme, when a data packet arrives at a sink, the sink’s electrical quantity will decrease by Qpacket , where Qpacket is the electrical quantity of a data packet. The sinks that have received more data packets will keep less electrical quantities. When the decline of its electrical quantity exceeds certain threshold, for example, 20Qpacket , the sink will broadcast its electrical quantity all over the network and attract less data packets in the future. This is a negative feedback mechanism and will result traffic load balance in the network. A theoretical analysis will be given later. For robustness, another scheme called Sink Failure Announcement is proposed. When sink κ stops working, other sinks will recognize this accident immediately. One of them will broadcast a control message that commands all nodes to update their memory space and rewrite Qκ (electrical quantity of sink κ) as zero. Then sink κ loses attraction to packets, no packets will be routed to sink κ following (7). When sink κ resumes and starts to work again, it will broadcast a control message to refresh Qκ in nodes’ memory. As a result, it begins to receive data packets again. To avoid communication congestion, Congestion Control scheme is proposed as follows. As dual channels are provided in our protocol, control messages would not collide with data packets, otherwise congestion control cannot be effective. When sinks detect congestion, they will broadcast control

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IV. G LOBAL I NFORMATIVE M ECHANISM A NALYSIS In this section, we will analyze our Global Informative Mechanism(GIM). As the central part of the mechanism is the adjustment of sinks’ electrical quantities, we will analyze the result of this adjustment. To simplify the analysis, we will theoretically study the case in which only two sinks are deployed in an infinite area. We choose the middle point of two sinks as original point and the line crossed two sinks as X axis. Two sinks are deployed at points XA (x1 , 0) and XB (x2 , 0). The electrical quantities of two sink are QA and QB . In our analysis, we will keep QB constant and adjust QA . For simplification, We introduce a parameter α = QA /QB . Then we analyze communication loads of two sinks. Actually, what we need to do is to solve inequation(11) and find the area Ω, in which Electrical Potential of any node related to sink A is smaller than Sink B. Ω = {(x, y) ∈ R × R : T (γ, A) ≤ T (γ, B)}

(11)

where γ is a sensor node, whose position is X = (x, y). According to (7) ,Ω can be presented as: Ω = {(x, y) ∈ R × R : −

QB QA ≤− } (12) |X − XA | |X − XB |

2.5 2 1.5 1 0.5 Y

messages to network. Specifically, when sink µ recognizes that communication traffic around it is congested, it will broadcast a control message that commends all nodes in the network to reduce Qµ by θ percent, where θ is a predetermined constant whose value depends on the situation. The worse the congestion is, the large θ is. After that, communication load of sink µ will be reduced to normal. µ cannot resume its electrical quantity until congestion has been eliminated. As described above, we adjust the electrical quantities of sinks ({Qj , j ∈ VS })to balance the communication load of sinks, even more to improve the performance of the network. Can the adjustment of {Qj , j ∈ VS } be effective to accomplish the balancing of communication load? We will analyze this in the following section.

As X = (x, y), XA = (x1 , 0), XB = (x2 , 0) and (13) is transformed to:  (x − x1 )2 + y 2  =α (x − x2 )2 + y 2

QA QB

β→

Center of Ω

Sink B

−0.5 −1 −1.5 −2 −2.5 −4

−3

−2

−1

0

1

X

Fig. 2.

Ω when α = 0.5

B. Communication loads of sink A and B are equal. Then, we analyze the situation when α < 1. The solution when α > 1 can be obtained by exchanging sink A and B. (14) can be transformed to: α(x1 − x2 ) 2 x1 − α2 x2 2 ) + y2 = ( ) (16) (x − 1 − α2 1 − α2 Obviously, this is a function of circle with center XC (xC , 0) and radius R: x1 − α2 x2 α|x1 − x2 | , R= (17) xC = 1 − α2 1 − α2 All nodes in the circle will transmit packets to sink A. However, not all nodes out of the circle will transmit packets to sink B. When packets cross the circle, they will be attracted to sink A, and can not travel to sink B any more. So area shielded by circle Ω should be also added to the communication load of sink A. Since we only analyze infinite situation, the ratio of A’s traffic load to B’s, which is denoted as ξ, can be calculated by following equation: β (18) ξ= 2π − β where β is shown in Fig.2 and can be calculated by geometry: β sin( ) 2

=

R |XB − XC |

(19)

(13)

=

(20)

= α,

=

α|x2 −x1 | 1−α2 −α2 x2 |x2 − x11−α | 2

To solve (12), we should solve (13) at first. QB QA = , X ∈R×R |X − XA | |X − XB |

Sink A

0

α

(21)

β = 2arcsin(α)

(22)

Then, we can get β: (14)

By introducing (22) to (18), we notice that:

Since the solution of (14) depends on the value of α, we should discuss α before we solve the equation. At first, when α = 1, the solution of (14) will be a straight line crossing original point: {(x, y) ∈ R × R : x = 0} (15) From above solution, we can get that all data packets sensed in the left plane will be transmitted to sink A and all data packets sensed in the right plane will be transmitted to sink

arcsin(α) (23) π − arcsin(α) From equation(23), we can say that ξ is a increasing function of α, which means that the ratio of traffic load is determined by the ratio of electrical quantities. This proves the fact that we can adjust a sink’s traffic load by altering its electrical quantity. Thus we conclude that Global Informative Mechanism is effective. ξ=

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of Nearest. In Fig.4, we can observe that the number of sensed events received by sinks in Nearest is as many as threefifths of NGIM and three-eighths of BLM. This fact illustrates that BLM provides much better reliability than Nearest. It also proves that GIM is effective to prolong the lifetime and improve the reliability of system. We can conclude that BLM protocol indeed greatly improve the performance of system in lifetime and reliability when the generation of events are not symmetrically distributed.

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BLM NGIM Nearest

1000

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VI. C ONCLUSION

4

Times of Simulation

Received Data Packets

Fig. 3.

In this paper, we proposed a load balanced routing (BLM) to prolong the lifetime of Wireless Sensor Networks. BLM is based on a distributed routing algorithm and a global informative mechanism. We introduced the concept ”potential value” in routing construction. Each node can accomplish the task assigned by system using local information. Meanwhile, sinks can adjust nodes’ behavior by altering their own parameters, and balance traffic load among the network effectively. Thanks to potential based routing scheme and global informative mechanism, BLM is robust to sink failure and adaptive to unbalanced traffic load. Furthermore, BLM can prolong the lifetime of the network significantly when events are generated in the network in an asymmetric way.

Lifetime

1600

BLM NGIM Nearest

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ACKNOWLEDGMENT

Data Packets Sensed Fig. 4.

Number of Received Packets

V. P ERFORMANCE E VALUATION In this section, we will compare BLM protocol with Nearest protocol and BLM without GIM (NGIM) through simulation. The simulation environment contains 900 sensor nodes and 4 sink nodes which are deployed in an area of size 100×100m2 . Each sensor node’s sense range and communication range are both 2m. Once a node sensed an event, it tries to transmit the event packet to a sink by three protocols listed above respectively. To simplify the simulation, we assume there is no error in the wireless transmission and only the transmission of data consumes sensor nodes’ energy. Events are generated in the network randomly and following two models. In the first model, events are generated symmetrically in the network. In the second model, events are generated in an asymmetrical way which means they are generated with bigger probability in some areas than others.Two metrics will be examined to evaluate the performance of systems: 1 Lifetime of the network 2 Number of packets received by sinks under given amount of events When the generation of events is symmetric in the network, our protocol is better than Nearest protocol, but the improvement is not significant. The simulation results in this model are not present in this paper for limitation upon number of pages. Simulation results in asymmetrical way are shown in Fig.3 and Fig.4. As in Fig.3, NGIM and Nearest achieve almost the same lifetime. However, BLM can obtain more than twice lifetime

This work was supported by the National Natural Science Foundation (60736027, 60574087), 863 High Tech Development Plan (2007AA01Z475, 2007AA04Z154, 2007AA01Z480, 2007AA01Z464) and 111 International Collaboration Program, of China. R EFERENCES [1] Soyturk, M. and Altilar, T., ”A Routing Algorithm for Mobile Multiple Sinks in Large-Scale Wireless Sensor Networks”, in Proc. of the 2nd International Symposium on Wireless Pervasive Computing, Feb. 2007. [2] Qing Huang; Yong Bai and Lan Chen, ”An Efficient Route Maintenance Scheme for Wireless Sensor Network with Mobile Sink” in Proc. of the IEEE 65th Vehicular Technology Conference, April 2007. [3] Haiyun Luo, Fan Ye, Jeffy Cheng, Songwu Lu and Lixia Zhang, ”TwoTier Data Dissemination in Large-Scale Wireless Sensor Networks” Wireless Networks 11, 161-175, 2005. [4] Soyturk, M. and Altilar, T., ”A Novel Stateless Energy-Efficient Routing Algorithm for Large-Scale Wireless Sensor Networks with Multiple Sinks” in Proc. of the IEEE Annual Wireless and Microwave Technology Conference, 2006. [5] Hyunyoung Lee, Klappenecker, A., Kyoungsook Lee, and Lan Lin ”Energy efficient data management for wireless sensor networks with data sink failure” in Proc. of the IEEE International Conference on Mobile Adhoc and Sensor Systems Conference, Nov. 2005. [6] Kiri, Yuichi; Sugano, Masashi; Murata and Masayuki. ”Self-Organized Data-Gathering Scheme for Multi-Sink Sensor Networks Inspired by Swarm Intelligence” in Proc. of the First International Conference on Self-Adaptive and Self-Organizing Systems, July 2007. [7] Takaaki Suzuki, Masaki Bandai and Takashi Watanabe ”DispersiveCast: Dispersive Packets Transmission to Multiple sinks for Energy Saving in Sensor Networks” in Proc. of 17th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, Sept. 2006. [8] Kalantari, M. and Shayman, M. ”Design optimization of multi-sink sensor networks by analogy to electrostatic theory” in Proc. of the IEEE Wireless International Communications and Networking Conference, April 2006.

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